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Inkeaw P, Srikummoon P, Chaijaruwanich J, Traisathit P, Awiphan S, Inchai J, Worasuthaneewan R, Theerakittikul T. Automatic Driver Drowsiness Detection Using Artificial Neural Network Based on Visual Facial Descriptors: Pilot Study. Nat Sci Sleep 2022; 14:1641-1649. [PMID: 36132745 PMCID: PMC9482962 DOI: 10.2147/nss.s376755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/26/2022] [Indexed: 11/23/2022] Open
Abstract
PURPOSE Driving while drowsy is a major cause of traffic accidents globally. Recent technologies for detection and alarm within automobiles for this condition are limited by their reliability, practicality, cost, and lack of clinical validation. In this study, we developed an early drowsiness detection algorithm and device based on the "gold standard brain biophysiological signal" and facial expression digital data. METHODS The data were obtained from 10 participants. Artificial neural networks (ANN) were adopted as the model. Composite features of facial descriptors (ie, eye aspect ratio (EAR), mouth aspect ratio (MAR), face length (FL), and face width balance (FWB)) extracted from two-second video frames were investigated. RESULTS The ANN combined with the EAR and MAR features had the most sensitivity (70.12%) while the ANN combined with the EAR, MAR, and FL features had the most accuracy and specificity (60.76% and 58.71%, respectively). In addition, by applying the discrete Fourier transform (DFT) to the composite features, the ANN combined with the EAR and MAR features again had the highest sensitivity (72.25%), while the ANN combined with the EAR, MAR, and FL features had the highest accuracy and specificity (60.40% and 54.10%, respectively). CONCLUSION The ANN with DFT combined with the EAR, MAR, and FL offered the best performance. Our direct driver sleepiness detection system developed from the integration of biophysiological information and internal validation provides a valuable algorithm, specifically toward alertness level.
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Affiliation(s)
- Papangkorn Inkeaw
- Data Science Research Center, Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Pimwarat Srikummoon
- Department of Statistics, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand.,Data Science Research Center, Department of Statistics, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Jeerayut Chaijaruwanich
- Data Science Research Center, Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand.,Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Patrinee Traisathit
- Department of Statistics, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand.,Data Science Research Center, Department of Statistics, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand.,Research Center in Bioresources for Agriculture, Industry and Medicine, Department of Statistics, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Suphakit Awiphan
- Data Science Research Center, Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand.,Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Juthamas Inchai
- Division of Pulmonary, Critical Care and Allergy, Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Ratirat Worasuthaneewan
- Sleep Disorder Center, Center for Medical Excellence, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Theerakorn Theerakittikul
- Division of Pulmonary, Critical Care and Allergy, Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand.,Sleep Disorder Center, Center for Medical Excellence, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand
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